Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

The goal of this work is to develop a Machine Learning model to predict the need for both invasive and non-invasive mechanical ventilation in intensive care unit (ICU) patients. Using the Philips eICU Research Institute (ERI) database, 2.6 million ICU patient data from 2010 to 2019 were analyzed.

Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay.

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features.

Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias.

Objectives: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance.

Design: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts.

A systematic literature review to identify ethical, legal, and social responsibilities of nonprofit organizations when funding clinical trials in pediatric cancer.

Nonprofit organizations (NPOs) play critical roles as funding sources, research partners, and disseminators of drug developments in pediatric cancer. Yet the literature provides limited guidance about ethical best practices when NPOs make trial funding decisions in this space. We conducted a systematic review of the literature indexed in PubMed and Web of Science to identify the ethical, legal, and social responsibilities of NPOs to four key stakeholder groups in funding pediatric cancer trials: (i) patients/families, (ii) researchers, (iii) industry sponsors, and (iv) donors.

Associations of self-reported eating disorder behaviors and personality in a college-educated sample.

Objective: In order to better understand factors motivating eating disorder (ED) behaviors and better identify persons at-risk for these behaviors, we sought to identify which personality domains and facets were associated with behaviors for weight control.

Methods: ED behavior information was gathered from the University of North Carolina Alumni Heart Study using the question, "have you ever used any of the following to lose weight?" Respondents endorsed any combination of the following: "Vomiting," "Fasting," "Laxatives," "Excessive physical exercise." Personality was measured using the Revised NEO Personality Inventory (NEO-PI-R).

Altered neuronal activity relationships between the pedunculopontine nucleus and motor cortex in a rodent model of Parkinson's disease.

The pedunculopontine nucleus (PPN) is a new deep brain stimulation (DBS) target for Parkinson's disease (PD), but little is known about PPN firing pattern alterations in PD. The anesthetized rat is a useful model for investigating the effects of dopamine loss on the transmission of oscillatory cortical activity through basal ganglia structures. After dopamine loss, synchronous oscillatory activity emerges in the subthalamic nucleus and substantia nigra pars reticulata in phase with cortical slow oscillations.